Summary
Objectives: The objectives of this research were to test the ability of classification algorithms
to predict the cause of death in the mortality data with unknown causes, to find association
between common causes of death, to identify groups of countries based on their common
causes of death, and to extract knowledge gained from data mining of the World Health
Organization mortality database.
Methods: The WEKA software version 3.5.3 was used for classification, clustering and association
analysis of the World Health Organization mortality database which contained 1,109,537
records. Three major steps were performed: Step 1 – preprocessing of data to convert
all records into suitable formats for each type of analysis algorithm; Step 2 – analyzing
data using the C4.5 decision tree and Naïve Bayes classification algorithm, K-means
clustering algorithm and Apriori association analysis algorithm; Step 3 – interpretation
of results and hypothesis testing after clustering analysis.
Results: Using a C4.5 decision tree classifier to predict cause of death, we obtained 440
leaf nodes that correctly classify death instances with an accuracy of 40.06%. Naïve
Bayes classification algorithm calculated probability of death from each disease that
correctly classify death instances with an accuracy of 28.13%. K means clustering
divided the data into four clusters with 189, 59, 65, 144 country-years in each cluster.
A Chi-square was used to test discriminate disease differences found in each cluster
which had different diseases as predominant causes of death. Apriori association analysis
produced association rules of linkage among cancer of the lung, hypertension and cerebrovascular
diseases. These were found in the top five leading causes of death with 99–100% confidence
level.
Conclusion: Classification tools produced the poorest results in predicting cause of death. Given
the inadequacy of variables in the WHO database, creation of a classification model
to predict specific cause of death was impossible. Clustering and association tools
yielded interesting results that could be used to identify new areas of interest in
mortality data analysis. This can be used in data mining analysis to help solve some
quality problems in mortality data.
Keywords
Mortality statistics - data mining - classification - clustering - association analysis